# importing libraries 
import pandas as pd 
import seaborn as sns 
import numpy as np 
import requests
import os 
import matplotlib.pyplot as plt 
#import missingno as msno

#job = pd.read_csv("city-of-melbourne-jobs-forecasts-by-small-area-2020-2040.csv")
job_data = pd.read_csv("https://data.melbourne.vic.gov.au/api/explore/v2.1/catalog/datasets/city-of-melbourne-jobs-forecasts-by-small-area-2020-2040/exports/csv?lang=en&timezone=Australia%2FSydney&use_labels=true&delimiter=%2C")
job = job_data.copy()

job.head()

# to get a basic understanding of variables and null values
job.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9114 entries, 0 to 9113
Data columns (total 5 columns):
 #   Column              Non-Null Count  Dtype 
---  ------              --------------  ----- 
 0   Geography           9114 non-null   object
 1   Year                9114 non-null   int64 
 2   Category            9114 non-null   object
 3   Industry Space Use  9114 non-null   object
 4   Value               9114 non-null   int64 
dtypes: int64(2), object(3)
memory usage: 356.1+ KB

# To see total numbers of rows and columns 
job.shape

(9114, 5)

# Renaming columns for ease, making them all lowercase. 
job = job.rename(columns = {"Geography":"city", "Year":"year", "Category":"category", "Industry Space Use": "industry", "Value": "value"} )

# In geography columns, to see which unique location it consists of
job.city.unique()

array(['City of Melbourne', 'Carlton', 'Docklands', 'East Melbourne',
       'Kensington', 'Melbourne (CBD)', 'Melbourne (Remainder)',
       'North Melbourne', 'Parkville', 'Port Melbourne', 'South Yarra',
       'Southbank', 'West Melbourne (Industrial)',
       'West Melbourne (Residential)'], dtype=object)

# to check unique years this data is from
job.year.unique()

array([2023, 2026, 2032, 2034, 2035, 2021, 2025, 2037, 2040, 2027, 2028,
       2036, 2041, 2029, 2031, 2038, 2024, 2030, 2039, 2022, 2033],
      dtype=int64)

# to understand uniqe categories in category columns 
# job.category.unique()
job

job.industry.unique()

array(['Accommodation', 'Admin and support services',
       'Agriculture and mining', 'Arts and recreation services',
       'Business services', 'Construction', 'Education and training',
       'Other services', 'Electricity, gas, water and waste services',
       'Finance and insurance', 'Food and beverage services',
       'Health care and social assistance',
       'Information media and telecommunications', 'Manufacturing',
       'Public administration and safety', 'Real estate services',
       'Rental and hiring services', 'Retail trade',
       'Transport, postal and storage', 'Wholesale trade',
       'Accommodation - Commercial', 'Education',
       'Entertainment - Hospitality', 'Entertainment - Other', 'Health',
       'Industrial', 'Office', 'Other - Employment', 'Retail',
       'Total jobs'], dtype=object)

#pop = pd.read_csv("city-of-melbourne-population-forecasts-by-small-area-2020-2040.csv")
pop = pd.read_csv("https://data.melbourne.vic.gov.au/api/explore/v2.1/catalog/datasets/city-of-melbourne-population-forecasts-by-small-area-2020-2040/exports/csv?lang=en&timezone=Australia%2FSydney&use_labels=true&delimiter=%2C")
# pop

pop.head()

pop.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 17052 entries, 0 to 17051
Data columns (total 5 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Geography  17052 non-null  object 
 1   Year       17052 non-null  int64  
 2   Gender     17052 non-null  object 
 3   Age        17052 non-null  object 
 4   Value      16989 non-null  float64
dtypes: float64(1), int64(1), object(3)
memory usage: 666.2+ KB

pop.shape

(17052, 5)

# Renaming columns for ease, making them all lowercase. 
pop = pop.rename(columns = {"Geography":"city", "Year":"year", "Gender":"gender", "Age": "age", "Value": "value"} )

pop.city.unique()

array(['City of Melbourne', 'Carlton', 'Docklands', 'East Melbourne',
       'Kensington', 'Melbourne (CBD)', 'Melbourne (Remainder)',
       'North Melbourne', 'Parkville', 'Port Melbourne', 'South Yarra',
       'Southbank', 'West Melbourne (Industrial)',
       'West Melbourne (Residential)'], dtype=object)

pop.gender.unique()

array(['Female', 'Male', 'Total', 'Not applicable'], dtype=object)

pop.age.unique()

array(['Age 0-4', 'Age 5-9', 'Age 10-14', 'Age 15-19', 'Age 20-24',
       'Age 25-29', 'Age 30-34', 'Age 35-39', 'Age 40-44', 'Age 45-49',
       'Age 50-54', 'Age 55-59', 'Age 60-64', 'Age 65-69', 'Age 70-74',
       'Age 75-79', 'Age 80-84', 'Age 85+', 'Average age',
       'Total population'], dtype=object)

pop.year.unique()

array([2021, 2023, 2026, 2028, 2034, 2037, 2038, 2024, 2025, 2040, 2031,
       2033, 2022, 2032, 2036, 2041, 2027, 2029, 2039, 2035, 2030],
      dtype=int64)

pop

# To check if there is any missing values in any columns 
pop.isnull().any()

city      False
year      False
gender    False
age       False
value      True
dtype: bool

# There are no null values in job dataset. However there is null (missing) values in population dataset in value column. 
# Lets find out how many null values are there in value column
print(pop.value.isnull().sum())


# Write more on why you chose to drop rows

63

# pop

pop = pop.dropna(axis= "rows")

pop.isnull().any()

city      False
year      False
gender    False
age       False
value     False
dtype: bool

# Choose all data where the gender is not applicable
pop = pop.set_index("gender")
other_pop = pop.loc[["Total", "Not applicable"],:]
other_pop                                         # saving total and not applicable data into differnet variable for further use. 
pop = pop.loc[["Female", "Male"],:]               # seperating male and female gender from the whole dataset
pop.reset_index(inplace=True)

pop       # df where gender = ['Male', 'Female']

other_pop    # df where all values from gender = ['total', 'not applicable']
other_pop.reset_index(drop=True)
other_pop

job.isnull().any()

city        False
year        False
category    False
industry    False
value       False
dtype: bool

job.head(5)

total_job_by_ind_space_use = job.set_index("industry").loc[['Total jobs'], : ]
job = job.set_index("industry").loc[['Accommodation', 'Admin and support services',
       'Agriculture and mining', 'Arts and recreation services',
       'Business services', 'Construction', 'Education and training',
       'Other services', 'Electricity, gas, water and waste services',
       'Finance and insurance', 'Food and beverage services',
       'Health care and social assistance',
       'Information media and telecommunications', 'Manufacturing',
       'Public administration and safety', 'Real estate services',
       'Rental and hiring services', 'Retail trade',
       'Transport, postal and storage', 'Wholesale trade',
       'Accommodation - Commercial', 'Education',
       'Entertainment - Hospitality', 'Entertainment - Other', 'Health',
       'Industrial', 'Office', 'Other - Employment', 'Retail'],:]
job.head(5)

total_job_by_ind_space_use.head(5)

job= job.reset_index()

job.shape

(8526, 5)

pop.shape

(11130, 5)

# changing data types of the variables if they are not accurate 
job.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8526 entries, 0 to 8525
Data columns (total 5 columns):
 #   Column    Non-Null Count  Dtype 
---  ------    --------------  ----- 
 0   industry  8526 non-null   object
 1   city      8526 non-null   object
 2   year      8526 non-null   int64 
 3   category  8526 non-null   object
 4   value     8526 non-null   int64 
dtypes: int64(2), object(3)
memory usage: 333.2+ KB

job.head(5)

sns.boxplot(x=job.value, y=job.industry, data=job )
plt.xscale('log')
plt.figure(figsize=(100,100))
plt.show()

# job.describe()

<Figure size 10000x10000 with 0 Axes>

# HANDLING OUTLIERS WITH PERCENTILE METHOD (WINSORIZATION)
for ind in ind_list:
    data=job.loc[job.industry.isin([ind])]
    upper_limit = data.value.quantile(0.85)  # tried 95 percentile but didnt remove many outliers so 85
    lower_limit = data.value.quantile(0.15)
    
    data[(data.value <= upper_limit) & (data.value >= lower_limit)]
    data_1= data.copy()
    data_1.value = np.where(data_1.value >= upper_limit,upper_limit, np.where(data_1.value <= lower_limit,lower_limit, data_1.value))
    job.loc[job.industry.isin([ind])] = data_1
    
sns.boxplot(x="value", y="industry", data=job )
plt.xscale('log')
plt.figure(figsize=(100,100))
plt.show()

<Figure size 10000x10000 with 0 Axes>

# Hisotgram of jobs with 20 bins 
sns.histplot(job.value,bins=20)
plt.xlabel("Value of  Jobs")
plt.figure(figsize=(10,10))
plt.show()

<Figure size 1000x1000 with 0 Axes>

# increasing bin size to see any major change 
sns.histplot(job.value, bins=100)

<Axes: xlabel='value', ylabel='Count'>

# Cumulative Counts and Empirical Cumulatie ditribution Function 
sns.histplot(job.value,stat="percent", cumulative=True, color="lightgray")
plt.show()

# data by industry 
# all indsutries name
data = (job
        .loc[:, ["industry", "year", "value","city"]]
        .set_index("industry")
        .sort_values("year")
        .reset_index()

       )  # selecting data based on condition. 
data.head(5)

# legend and colours  settings
# This is just to show users the legend based on city. 
p = sns.scatterplot(data=data,x="year", y="value", hue="city", legend=True )
sns.move_legend(p, "upper right", bbox_to_anchor=(1.7, 1))

# City of Melbourne and Mebourne CSB will see the sharpest rise in jobs

# axis code for subplot location 
fig, axs = plt.subplots(7, 4)  # sharex="all",sharey="all" removed
fig.suptitle('Scatterplot for No. of Jobs per city', fontsize=16)
fig.set_figwidth(20)
fig.set_figheight(20)


for ax in axs.flat:
    ax.set(xlabel='year', ylabel='value')
    # ax.set_xticklabels([])


# Hide x labels and tick labels for top plots and y ticks for right plots.
for ax in axs.flat:
    ax.label_outer()

# code that goes through names 
# of all industries from ind_list 
# and draws scatterplots
x = 0
while x < 28:
    x = 0
    name = ind_list[x]
    for i in range(7):
        for j in range(4):        
            axs[i,j].set_title(name)
            sns.scatterplot(data=data.loc[data.industry.isin([name])],x="year", y="value", hue="city", legend=False,ax=axs[i,j])
           
            
            # print(i, j ,name)
            x+=1
            name = ind_list[x]

avg_age_df = pop[pop.age.isin(["Average age"])]
avg_age_df

pop.age.unique()

array(['Age 0-4', 'Age 5-9', 'Age 10-14', 'Age 15-19', 'Age 20-24',
       'Age 25-29', 'Age 30-34', 'Age 35-39', 'Age 40-44', 'Age 45-49',
       'Age 50-54', 'Age 55-59', 'Age 60-64', 'Age 65-69', 'Age 70-74',
       'Age 75-79', 'Age 80-84', 'Age 85+', 'Average age'], dtype=object)

pop.tail(5)   # before removing averge age rows

# removing the rwos where the age is avergae age
pop = pop.set_index("age")
pop = pop.drop(index=("Average age"))

pop = pop.reset_index()

pop.tail()   # after removing average age rows in age col

# female data 
female_data = pop[pop["gender"]=="Female"]
female_data

age_list = ['Age 0-4', 'Age 5-9', 'Age 10-14', 'Age 15-19', 'Age 20-24',
       'Age 25-29', 'Age 30-34', 'Age 35-39', 'Age 40-44', 'Age 45-49',
       'Age 50-54', 'Age 55-59', 'Age 60-64', 'Age 65-69', 'Age 70-74',
       'Age 75-79', 'Age 80-84', 'Age 85+']

data = (female_data
       .groupby(["age", "city"])
       .size()
       .rename("counts")
       .reset_index())
data
plt.figure(figsize=(8,8))
sns.barplot(data= data, y="counts", x="age", palette="Paired", hue="city")
plt.xticks(rotation=90)

plt.show()


# From this data we can see that the age demographic that make up most female population in this dataset is age 25-29 making up population of 23164.00

pop

(pop
      .loc[:, ["gender", "value"]]
      .groupby("gender")
      .size())


# The dataset contains equal number of observations for both male and female

gender
Female    5292
Male      5292
dtype: int64

(pop
.loc[:, ['age', 'city', 'gender']]
.groupby(["gender", 'age'])
.size()
.rename("counts")
.reset_index())

# Our dataset contains equal distribution of both male and female in each 
# age group

(pop
.loc[:, ['gender', 'year', 'value','city']]
.groupby(["city", "year"])
.size()
.rename("population_count")
.reset_index)

<bound method Series.reset_index of city                          year
Carlton                       2021    36
                              2022    36
                              2023    36
                              2024    36
                              2025    36
                                      ..
West Melbourne (Residential)  2037    36
                              2038    36
                              2039    36
                              2040    36
                              2041    36
Name: population_count, Length: 294, dtype: int64>

(pop
.loc[:, ['gender', 'year', 'value','city']]
.groupby(["gender", "year"])
.size()
.rename("population_count")
.reset_index)

<bound method Series.reset_index of gender  year
Female  2021    252
        2022    252
        2023    252
        2024    252
        2025    252
        2026    252
        2027    252
        2028    252
        2029    252
        2030    252
        2031    252
        2032    252
        2033    252
        2034    252
        2035    252
        2036    252
        2037    252
        2038    252
        2039    252
        2040    252
        2041    252
Male    2021    252
        2022    252
        2023    252
        2024    252
        2025    252
        2026    252
        2027    252
        2028    252
        2029    252
        2030    252
        2031    252
        2032    252
        2033    252
        2034    252
        2035    252
        2036    252
        2037    252
        2038    252
        2039    252
        2040    252
        2041    252
Name: population_count, dtype: int64>

sns.boxplot(x=pop.value, y=pop.city, data=pop )
plt.xscale('log')
plt.figure(figsize=(100,100))
plt.show()

<Figure size 10000x10000 with 0 Axes>

city_names = list(pop.city.unique())
len(city_names)
city_names

['City of Melbourne',
 'Carlton',
 'Docklands',
 'East Melbourne',
 'Kensington',
 'Melbourne (CBD)',
 'Melbourne (Remainder)',
 'North Melbourne',
 'Parkville',
 'Port Melbourne',
 'South Yarra',
 'Southbank',
 'West Melbourne (Industrial)',
 'West Melbourne (Residential)']

# HANDLING OUTLIERS WITH PERCENTILE METHOD (WINSORIZATION)
for city in city_names:
    data=pop.loc[pop.city.isin([city])]
    upper_limit = data.value.quantile(0.90)  # tried 95 percentile but didnt remove many outliers so 85
    lower_limit = data.value.quantile(0.10)
    
    data[(data.value <= upper_limit) & (data.value >= lower_limit)]
    data_1= data.copy()
    data_1.value = np.where(data_1.value >= upper_limit,upper_limit, np.where(data_1.value <= lower_limit,lower_limit, data_1.value))
    pop.loc[pop.city.isin([city])] = data_1
    
sns.boxplot(x=pop.value, y=pop.city, data=pop )
plt.xscale('log')
plt.figure(figsize=(100,100))
plt.show()

<Figure size 10000x10000 with 0 Axes>

pop

sns.scatterplot(data=pop, x="year", y="value", hue="gender")
plt.title("Scatterplot for population change by year (overall-based on gender) ")
plt.show()

plt.figure(figsize=(8,8))
p = sns.scatterplot(data=pop, x="year", y="value", hue="age")
plt.title("Scatterplot for population change by year (overall-based on age group) ")
sns.move_legend(p, "upper right", bbox_to_anchor=(1.3, 1))

plt.show()

city_names = list(pop.city.unique())
len(city_names)
city_names

['City of Melbourne',
 'Carlton',
 'Docklands',
 'East Melbourne',
 'Kensington',
 'Melbourne (CBD)',
 'Melbourne (Remainder)',
 'North Melbourne',
 'Parkville',
 'Port Melbourne',
 'South Yarra',
 'Southbank',
 'West Melbourne (Industrial)',
 'West Melbourne (Residential)']

pop

# axis code for subplot location 
fig, axs = plt.subplots(7, 2)  # sharex="all",sharey="all" removed
fig.suptitle('Scatterplot for No. of Jobs per city', fontsize=16)
fig.set_figwidth(15)
fig.set_figheight(15)


for ax in axs.flat:
    ax.set(xlabel='year', ylabel='value')
    # ax.set_xticklabels([])


# Hide x labels and tick labels for top plots and y ticks for right plots.
for ax in axs.flat:
    ax.label_outer()

# code that goes through names 
# of all industries from ind_list 
# and draws scatterplots
x = 0
while x <= 14:
    x = 0
    name = city_names[x]
    for i in range(7):
        for j in range(2):        
            axs[i,j].set_title(name)
            sns.scatterplot(data=pop.loc[pop.city.isin([name])],x="year", y="value", hue="gender", legend=False,ax=axs[i,j],alpha=0.6)
           
            
            # print(i, j ,name)
            x+=1
            name = city_names[x]

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
Cell In[3218], line 32
     30 # print(i, j ,name)
     31 x+=1
---> 32 name = city_names[x]

IndexError: list index out of range

# axis code for subplot location 
fig, axs = plt.subplots(7, 2)  # sharex="all",sharey="all" removed
# fig.suptitle('Scatterplot for No. of Jobs per city')
fig.set_figwidth(15)
fig.set_figheight(40)


for ax in axs.flat:
    ax.set(xlabel='year', ylabel='value')
    # ax.set_xticklabels([])


# Hide x labels and tick labels for top plots and y ticks for right plots.
for ax in axs.flat:
    ax.label_outer()

# code that goes through names 
# of all industries from ind_list 
# and draws scatterplots
x = 0
while x <= 14:
    x = 0
    name = city_names[x]
    for i in range(7):
        for j in range(2):        
            axs[i,j].set_title(name)
            sns.scatterplot(data=pop.loc[pop.city.isin([name])],x="year", y="value", hue="age", legend=False,ax=axs[i,j],alpha=0.6)
           
            
            # print(i, j ,name)
            x+=1
            name = city_names[x]

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
Cell In[3225], line 32
     30 # print(i, j ,name)
     31 x+=1
---> 32 name = city_names[x]

IndexError: list index out of range

job.value.shape

(8526,)

pop.value.shape

(10584,)

job.city.unique()

array(['City of Melbourne', 'Carlton', 'Docklands', 'East Melbourne',
       'Kensington', 'Melbourne (CBD)', 'Melbourne (Remainder)',
       'North Melbourne', 'Parkville', 'Port Melbourne', 'South Yarra',
       'Southbank', 'West Melbourne (Industrial)',
       'West Melbourne (Residential)'], dtype=object)

pop.year.unique()

array([2021, 2023, 2026, 2028, 2034, 2037, 2038, 2031, 2033, 2040, 2041,
       2029, 2039, 2024, 2030, 2022, 2027, 2035, 2036, 2025, 2032],
      dtype=int64)

pop.city.unique()

array(['City of Melbourne', 'Carlton', 'Docklands', 'East Melbourne',
       'Kensington', 'Melbourne (CBD)', 'Melbourne (Remainder)',
       'North Melbourne', 'Parkville', 'Port Melbourne', 'South Yarra',
       'Southbank', 'West Melbourne (Industrial)',
       'West Melbourne (Residential)'], dtype=object)

city_names

['City of Melbourne',
 'Carlton',
 'Docklands',
 'East Melbourne',
 'Kensington',
 'Melbourne (CBD)',
 'Melbourne (Remainder)',
 'North Melbourne',
 'Parkville',
 'Port Melbourne',
 'South Yarra',
 'Southbank',
 'West Melbourne (Industrial)',
 'West Melbourne (Residential)']

# model example - using carlton as the city for now
df = pop.loc[pop['city']=='Carlton']
df=df.drop(columns = ["age","gender","city"])
# df
from sklearn.linear_model import LinearRegression
x = df.iloc[:, 0].values.reshape(-1, 1)
y = df.iloc[:, 1].values.reshape(-1, 1)
model = LinearRegression().fit(x, y)
y_pred = model.predict([[2041]])
y_pred

array([[847.05675806]])

a = pop.loc[pop.city.isin(['Carlton'])]
a[a.year==int("2037")].value.mean()

789.4583333333334

# main model code 
def main_model():
    print(city_names)
    print()
    city = str(input("Choose from the following list of cities : "))
    print()
    year = str(input("Enter the year you would like the population prediction of: "))
    

    # SGS's data that already exist
    if 2021 <= int(year) <= 2032:
        a = pop.loc[pop.city.isin([city])]
        print("SGS Prediction Value: ", round(a[a.year==int(year)].value.mean(),2))

    # my model prediction
    df = pop.loc[pop.city.isin([city])]
    df=df.drop(columns = ["age","gender","city"])
    # df
    from sklearn.linear_model import LinearRegression
    x = df.iloc[:, 0].values.reshape(-1, 1)
    y = df.iloc[:, 1].values.reshape(-1, 1)
    model = LinearRegression().fit(x, y)
    y_pred = model.predict([[int(year)]])
    print(f"Models's predicted value : {round(y_pred[0][0],2)}")
        
main_model()

['City of Melbourne', 'Carlton', 'Docklands', 'East Melbourne', 'Kensington', 'Melbourne (CBD)', 'Melbourne (Remainder)', 'North Melbourne', 'Parkville', 'Port Melbourne', 'South Yarra', 'Southbank', 'West Melbourne (Industrial)', 'West Melbourne (Residential)']

SGS Prediction Value:  463.67
Models's predicted value : 467.58

	Geography	Year	Category	Industry Space Use	Value
0	City of Melbourne	2023	Jobs by industry	Accommodation	10286
1	City of Melbourne	2026	Jobs by industry	Accommodation	11631
2	City of Melbourne	2032	Jobs by industry	Accommodation	13207
3	City of Melbourne	2034	Jobs by industry	Accommodation	13420
4	City of Melbourne	2035	Jobs by industry	Accommodation	13529

	Geography	Year	Gender	Age	Value
0	City of Melbourne	2021	Female	Age 0-4	1943.0
1	City of Melbourne	2023	Female	Age 0-4	2477.0
2	City of Melbourne	2026	Female	Age 0-4	3595.0
3	City of Melbourne	2028	Female	Age 0-4	4083.0
4	City of Melbourne	2034	Female	Age 0-4	5242.0

	city	year	age	value
gender
Total	City of Melbourne	2021	Age 0-4	4051.0
Total	City of Melbourne	2021	Age 20-24	33134.0
Total	City of Melbourne	2021	Age 30-34	21485.0
Total	City of Melbourne	2021	Age 50-54	4939.0
Total	City of Melbourne	2021	Age 65-69	3698.0
...	...	...	...	...
Not applicable	West Melbourne (Residential)	2029	Total population	11673.0
Not applicable	West Melbourne (Residential)	2032	Total population	13191.0
Not applicable	West Melbourne (Residential)	2035	Total population	14820.0
Not applicable	West Melbourne (Residential)	2039	Total population	14794.0
Not applicable	West Melbourne (Residential)	2041	Total population	14814.0

	city	year	category	value
industry
Accommodation	City of Melbourne	2023	Jobs by industry	10286
Accommodation	City of Melbourne	2026	Jobs by industry	11631
Accommodation	City of Melbourne	2032	Jobs by industry	13207
Accommodation	City of Melbourne	2034	Jobs by industry	13420
Accommodation	City of Melbourne	2035	Jobs by industry	13529

	city	year	category	value
industry
Total jobs	City of Melbourne	2022	Jobs by industry	473514
Total jobs	City of Melbourne	2023	Jobs by industry	483587
Total jobs	City of Melbourne	2026	Jobs by industry	519577
Total jobs	City of Melbourne	2027	Jobs by industry	530590
Total jobs	City of Melbourne	2030	Jobs by industry	563548

	industry	year	value	city
0	Office	2021	78886.10	Melbourne (CBD)
1	Health	2021	308.00	Kensington
2	Retail trade	2021	83.00	West Melbourne (Industrial)
3	Admin and support services	2021	2372.85	City of Melbourne
4	Education	2021	370.90	East Melbourne

	gender	city	year	age	value
1753	Female	City of Melbourne	2023	Average age	33.0
1754	Female	City of Melbourne	2028	Average age	33.0
1755	Female	City of Melbourne	2029	Average age	33.0
1756	Female	City of Melbourne	2030	Average age	33.0
1757	Female	City of Melbourne	2031	Average age	33.0
...	...	...	...	...	...
11125	Male	West Melbourne (Residential)	2024	Average age	31.0
11126	Male	West Melbourne (Residential)	2026	Average age	32.0
11127	Male	West Melbourne (Residential)	2027	Average age	32.0
11128	Male	West Melbourne (Residential)	2037	Average age	33.0
11129	Male	West Melbourne (Residential)	2038	Average age	33.0

	age	gender	city	year	value
10579	Age 85+	Male	West Melbourne (Residential)	2025	2.0
10580	Age 85+	Male	West Melbourne (Residential)	2027	3.0
10581	Age 85+	Male	West Melbourne (Residential)	2031	5.0
10582	Age 85+	Male	West Melbourne (Residential)	2038	8.0
10583	Age 85+	Male	West Melbourne (Residential)	2041	8.0